Handbook of Statistical Data Editing and Imputation

A practical, one-stop reference on the theory and applications of
statistical data editing and imputation techniques

Collected survey data are vulnerable to error. In particular,
the data collection stage is a potential source of errors and
missing values. As a result, the important role of statistical data
editing, and the amount of resources involved, has motivated
considerable research efforts to enhance the efficiency and
effectiveness of this process. Handbook of Statistical Data Editing
and Imputation equips readers with the essential statistical
procedures for detecting and correcting inconsistencies and filling
in missing values with estimates. The authors supply an easily
accessible treatment of the existing methodology in this field,
featuring an overview of common errors encountered in practice and
techniques for resolving these issues.

The book begins with an overview of methods and strategies for
statistical data editing and imputation. Subsequent chapters
provide detailed treatment of the central theoretical methods and
modern applications, with topics of coverage including:

Localization of errors in continuous data, with an outline of
selective editing strategies, automatic editing for systematic and
random errors, and other relevant state-of-the-art methods

Extensions of automatic editing to categorical data and integer
data

The basic framework for imputation, with a breakdown of key
methods and models and a comparison of imputation with the
weighting approach to correct for missing values

More advanced imputation methods, including imputation under
edit restraints

Throughout the book, the treatment of each topic is presented in
a uniform fashion. Following an introduction, each chapter presents
the key theories and formulas underlying the topic and then
illustrates common applications. The discussion concludes with a
summary of the main concepts and a real-world example that
incorporates realistic data along with professional insight into
common challenges and best practices.

Handbook of Statistical Data Editing and Imputation is an
essential reference for survey researchers working in the fields of
business, economics, government, and the social sciences who
gather, analyze, and draw results from data. It is also a suitable
supplement for courses on survey methods at the upper-undergraduate
and graduate levels.

Ton De Waal, PhD, is Head of the Department of Methodology at
Statistics Netherlands, where he has also worked at the Division of
Business Statistics. Dr. de Waal has written numerous papers in his
areas of research interest, which include statistical data editing
and imputation for business surveys and statistical disclosure
control.

Jeroen Pannekoek, PhD, is Senior Researcher in the Department of
Methodology at Statistics Netherlands, where he currently leads the
research program on data processing methodologies. He has published
several papers on discrete data models, measurement errors,
interviewer effects, and disclosure control methods.

Sander Scholtus, MSc, is Researcher in the Department of
Methodology at Statistics Netherlands. He has conducted extensive
research on heuristic methods and algorithms for detecting and
correcting errors in survey data.

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